A question answer system answers questions posed in a natural language format by applying advanced natural language processing, information retrieval, knowledge representation, automated reasoning, and machine-learning technologies. Question answer systems differ from typical document search technologies because document search technologies return a list of documents ranked in order of relevance to a word query, whereas question answer systems receive a question expressed in a natural language, seeks to understand the question in much greater detail, and returns a precise answer to the question.
System developers may train question answer systems to specific domains to provide more relevant answers to domain-specific questions (e.g., financial domain, legal domain, etc.). Training a question answer system for a new domain, however, is time consuming. During the training process, a question answer system may ingest source documents and create feature vectors that map a document's multiple features. In turn, domain experts may label a portion of the feature vectors to train the question answer system's logistic regression models by generating a “hyperplane” that separates “yes” answers from “no” answers.
Many source documents, however, may include time-dated information due to changing world conditions. For example, publishers typically publish textbooks every few years and publish journals on a monthly or quarterly basis. As such, question answer systems may require retraining at particular points in time.